The proposed model is trained for 150 epochs on a single TITAN RTX using batch size of 32, the Adam optimizer [1]} with \((\beta _1, \beta _2, \epsilon ) = (0.9, 0.999, 10^{-8})\) , and the loss function presented in (REF ). The initial learning rate is \(1.2*10^{-4}\) , but from epoch 10 the learning is reduced by \(6\%\)  per 5 epochs. We set the number of scales \(n\)  in (REF ) to 5, weight loss coefficients \(\mu , \theta \)  to \(1.0\) , and the scale weight losses \(\gamma ^1, \gamma ^2, \gamma ^3, \gamma ^4, \gamma ^5\)  to \(1.0, 0.75, 0.5, 0.25\)  and \(0.125\)  respectively. To remove the effect of the arbitrary scale of the COLMAP points, we center and normalize the 3D inputs to a unit sphere before the training. During training, we augment the input RGB and ground truth depth images using random rotation ([-5.0, +5.0] degrees), horizontal flip, rectangular window droppings, and colorization (RGB only).
